
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowerCamelCase_    :			Dict    =	[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]







def 							A__       (    lowerCamelCase    )	->						List[Any]:
				for pegasus_name, hf_name in PATTERNS:
								UpperCamelCase_:				List[Any]						=    k.replace(lowerCamelCase  ,     lowerCamelCase    )
				return k







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						PegasusForConditionalGeneration:
				UpperCamelCase_:				Any						=    DEFAULTS.copy()
				cfg_kwargs.update(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    PegasusConfig(**lowerCamelCase    )
				UpperCamelCase_:				Any						=    PegasusForConditionalGeneration(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    torch_model.model.state_dict()
				UpperCamelCase_:				Union[str, Any]						=    {}
				for k, v in tf_weights.items():
								UpperCamelCase_:				List[str]						=    rename_state_dict_key(lowerCamelCase    )
								if new_k not in sd:
												raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''    )

								if "dense" in k or "proj" in new_k:
												UpperCamelCase_:				List[Any]						=    v.T
								UpperCamelCase_:				List[str]						=    torch.tensor(lowerCamelCase  ,     dtype=sd[new_k].dtype    )
								assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
				# make sure embedding.padding_idx is respected
				UpperCamelCase_:				Union[str, Any]						=    torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]    )
				UpperCamelCase_:				Optional[int]						=    mapping["""shared.weight"""]
				UpperCamelCase_:				int						=    mapping["""shared.weight"""]
				UpperCamelCase_:				Dict						=    {k: torch.zeros_like(lowerCamelCase    ) for k, v in sd.items() if k.endswith("""bias"""    ) and k not in mapping}
				mapping.update(**lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    torch_model.model.load_state_dict(lowerCamelCase  ,     strict=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    [
				    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
				]
				assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
				assert extra == [], F'''no matches found for the following tf keys {extra}'''
				return torch_model







def 							A__       (    lowerCamelCase="./ckpt/aeslc/model.ckpt-32000"    )	->						Dict:
				UpperCamelCase_:				Dict						=    tf.train.list_variables(lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    {}
				UpperCamelCase_:				Optional[Any]						=    ["""Adafactor""", """global_step"""]
				for name, shape in tqdm(lowerCamelCase  ,     desc="""converting tf checkpoint to dict"""    ):
								UpperCamelCase_:				List[str]						=    any(pat in name for pat in ignore_name    )
								if skip_key:
												continue
								UpperCamelCase_:				Optional[Any]						=    tf.train.load_variable(lowerCamelCase  ,     lowerCamelCase    )
								UpperCamelCase_:				Union[str, Any]						=    array
				return tf_weights







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				# save tokenizer first
				UpperCamelCase_:				Any						=    Path(lowerCamelCase    ).parent.name
				UpperCamelCase_:				Union[str, Any]						=    task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
				UpperCamelCase_:				str						=    PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""  ,     model_max_length=lowerCamelCase    )
				assert tok.model_max_length == desired_max_model_length
				tok.save_pretrained(lowerCamelCase    )

				# convert model
				UpperCamelCase_:				Any						=    get_tf_weights_as_numpy(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    task_specific_params[F'''summarization_{dataset}''']
				if dataset == "large":
								UpperCamelCase_:				Dict						=    task_specific_params
				UpperCamelCase_:				Any						=    convert_pegasus(lowerCamelCase  ,     lowerCamelCase    )
				torch_model.save_pretrained(lowerCamelCase    )
				UpperCamelCase_:				int						=    torch_model.state_dict()
				sd.pop("""model.decoder.embed_positions.weight"""    )
				sd.pop("""model.encoder.embed_positions.weight"""    )
				torch.save(lowerCamelCase  ,     Path(lowerCamelCase    ) / """pytorch_model.bin"""    )


if __name__ == "__main__":
							lowerCamelCase_    :			str    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
							parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCamelCase_    :			List[str]    =	parser.parse_args()
							if args.save_dir is None:
														lowerCamelCase_    :			List[str]    =	Path(args.tf_ckpt_path).parent.name
														lowerCamelCase_    :			int    =	os.path.join("""pegasus""", dataset)
							convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[Any]						=    len(lowerCamelCase    ) // 2

				# choose the middle 3 elements
				UpperCamelCase_:				Tuple						=    lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]    ) == 2:
												m -= 1
								return peak(lst[m:]    )

				# decreasing
				else:
								if len(lst[:m]    ) == 2:
												m += 1
								return peak(lst[:m]    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






